The monitoring of harmful algae is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive and limited in practice. The automatic classification of algae cell images and the identification of harmful algae images were realized by the combination of multiple Convolutional Neural Networks (CNNs) and deep learning techniques based on transfer learning in this work. 11 common harmful and 31 harmless algae genera were collected as input samples, the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify algae images, and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful algae which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful algae which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful algae could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful algae and greatly reduces the workload of professional personnel.

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This paper presents chip implementation of the integrated neural recording and stimulation system with stimulation-induced artifact suppression. The implemented chip consists of low-power neural recording circuits, stimulation circuits, and action potential detection circuits. These circuits constitute a closed-loop simultaneous neural recording and stimulation system for biomedical devices, and a proposed artifact suppression technique is used in the system. Moreover, this paper also presents the measurement and experiment results of the implemented 4-to-4 channel neural recording and stimulation chip with 0.18 µm CMOS technology. The function and efficacy of simultaneous neural recording and stimulation is validated in both in vivo and animal experiments.

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